Direct Divergence Approximation between Probability Distributions and Its Applications in Machine Learning
Direct Divergence Approximation between Probability Distributions and Its Applications in Machine Learning
Approximating a divergence between two probability distributions from their samples is a fundamental challenge in statistics, information theory, and machine learning. A divergence approximator can be used for various purposes, such as two-sample homogeneity testing, change-point detection, and class-balance estimation. Furthermore, an approximator of a divergence between the joint distribution …